1 code implementation • Findings (NAACL) 2022 • Yang Liu, Jinpeng Hu, Xiang Wan, Tsung-Hui Chang
Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes.
1 code implementation • Findings (NAACL) 2022 • Jinpeng Hu, He Zhao, Dan Guo, Xiang Wan, Tsung-Hui Chang
In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting.
Cross-Domain Named Entity Recognition named-entity-recognition +2
no code implementations • 4 Apr 2023 • Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, Yue Zhang
To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.
no code implementations • 15 Oct 2022 • Jinpeng Hu, Zhihong Chen, Yang Liu, Xiang Wan, Tsung-Hui Chang
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists.
1 code implementation • 15 Sep 2022 • Zhihong Chen, Yuhao Du, Jinpeng Hu, Yang Liu, Guanbin Li, Xiang Wan, Tsung-Hui Chang
Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training.
1 code implementation • Findings (ACL) 2022 • Yang Liu, Jinpeng Hu, Xiang Wan, Tsung-Hui Chang
We argue that relation information can be introduced more explicitly and effectively into the model.
1 code implementation • NAACL 2022 • Jinpeng Hu, Yaling Shen, Yang Liu, Xiang Wan, Tsung-Hui Chang
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text.
Ranked #1 on Named Entity Recognition (NER) on WNUT 2016
1 code implementation • ACL 2022 • Jinpeng Hu, Zhuo Li, Zhihong Chen, Zhen Li, Xiang Wan, Tsung-Hui Chang
To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i. e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation.
1 code implementation • Findings (ACL) 2021 • Jinpeng Hu, Jianling Li, Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan, Tsung-Hui Chang
In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph.